Inference Time Context Sparsity: Illusion or Opportunity?
The paper discusses the role of context sparsity in large language model (LLM) efficiency. It argues that the constraints of compute and memory in attention mechanisms are artificial and that extreme context sparsity could enhance LLM inference. The authors provide empirical evidence supporting their position and suggest that current hardware can leverage this sparsity for significant performance gains.
- ▪The paper presents a position that the constraints of compute and memory in LLMs are unnecessary.
- ▪Empirical studies show that current LLMs are robust to inference-time decode sparsity across various tasks.
- ▪Sparse decode kernels can accelerate large-context processing by up to 10x at high sparsity levels on existing hardware.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24168 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | l8QUA5X3U_2l |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24168 (cs) [Submitted on 22 May 2026] Title:Inference Time Context Sparsity: Illusion or Opportunity? Authors:Sahil Joshi, Prithvi Dixit, Agniva Chowdhury, Anshumali Shrivastava, Joseph E. Gonzalez, Ion Stoica, Kumar Krishna Agrawal, Aditya Desai View a PDF of the paper titled Inference Time Context Sparsity: Illusion or Opportunity?, by Sahil Joshi and 7 other authors View PDF HTML (experimental) Abstract:Sparsity has long been a central theme in LLM efficiency, but its role in context processing remains unresolved. As LLM workloads shift toward longer contexts and agentic interactions, the compute and memory bottlenecks of attention become increasingly critical, raising the question of whether these constraints are fundamental.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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